from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.dm.dm_station_add_push_monitor_detail_dt import jms_dm__dm_station_add_push_monitor_detail_dt
from jms.time_sensor.time_after_05_45 import time_after_05_45

# 配置所依赖的表所处的[任务名字]及[任务所在包的位置]

jms_dm__dm_station_add_push_monitor_summary_dt = SparkSqlOperator(
    task_id='jms_dm__dm_station_add_push_monitor_summary_dt',
    task_concurrency=1,
    pool_slots=1,
    master='yarn',
    execution_timeout=timedelta(minutes=30),
    email=['wangmenglei@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_station_add_push_monitor_summary_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_station_add_push_monitor_summary_dt/execute.sql',
    driver_memory='6G',
    driver_cores=4,
    executor_cores=4,
    executor_memory='30G',
    num_executors=10,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          #'spark.dynamicAllocation.maxExecutors': 60,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.maxExecutors': 27,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 180,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '2G',  # 堆外内存
          'spark.sql.shuffle.partitions': 700,
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          'spark.network.timeout': 900,
          'spark.core.connection.ack.wait.timeout': 300,
          },
    yarn_queue='pro',
)
# 设置依赖
jms_dm__dm_station_add_push_monitor_summary_dt << [
    jms_dm__dm_station_add_push_monitor_detail_dt

,time_after_05_45
]
